Darwin-180B-RSI Tops MMLU-Pro and GPQA Diamond on AIhoster's Independent Open-Model Benchmark Board

VIDRAFT's Darwin-180B-RSI, a 180-billion-parameter open-weight model, has taken the top spot on both MMLU-Pro (88.1%) and GPQA Diamond (94.4%) on AIhoster's independent benchmark aggregation board as of its 2026-10-19 update, edging out models with far larger parameter counts such as DeepSeek-V4-Pro and Kimi-K3. The GPQA figure was achieved with majority voting over up to 16 samples, while sibling VIDRAFT model Darwin-397B-ZTC reached 93.4% with greedy decoding. AIhoster aggregates publicly reported scores without recomputing them, and VIDRAFT has not disclosed architecture, training data, or licensing details.

TL;DR: VIDRAFT's Darwin-180B-RSI, a 180-billion-parameter open model, has claimed the 1 position on both the MMLU-Pro and GPQA Diamond leaderboards as tracked by Russian benchmark aggregator AIhoster — scoring 88.1% and 94.4% respectively, outperforming models with parameter counts an order of magnitude larger. Developers interested in frontier reasoning capabilities in an open-weight format should take note. Darwin-180B-RSI is a 180B-parameter open-weight language model developed by VIDRAFT 비드래프트 , a Korean Pre-AGI AI startup. It is published under the Hugging Face repository identifier FINAL-Bench/Darwin-180B-RSI . AIhoster is an independent Russian benchmark aggregation site that collects publicly reported evaluation results from model cards, papers, and leaderboards — without recomputing or normalizing scores into a composite index. Each benchmark entry is tied to an exact repository ID and a specific evaluation configuration, making comparisons methodologically transparent. As of the AIhoster update on 2026-10-19, Darwin-180B-RSI holds: Notably, VIDRAFT has multiple Darwin-family models appearing in the GPQA Diamond rankings simultaneously: Darwin-397B-ZTC 3 at 93.4% and Darwin-398B-JGOS 9 at 90.9% , suggesting an active, iterative development pipeline across different model scales and inference configurations. At a conceptual level, Darwin-180B-RSI appears to be a large-scale transformer-based language model optimized for extended reasoning. A few things can be inferred from the publicly disclosed evaluation configurations: No architectural specifics, training hyperparameters, or dataset details are publicly disclosed in the source material. All figures below are sourced directly from AIhoster's leaderboard updated 2026-10-19 , which in turn cites the Darwin-180B-RSI model card. MMLU-Pro multi-domain expert knowledge; higher accuracy = better : | Rank | Model | Score | Params | |---|---|---|---| | 1 | Darwin-180B-RSI | 88.1% | 180B | | 2 | MiniMax-M2.1 | 88.0% | 229B | | 3 | Intern-S2-Preview | 88.0% | 36.1B | | 4 | Qwen3.5-397B-A17B | 87.8% | 403B | | 5 | DeepSeek-V4-Pro | 87.5% | 1.6T | GPQA Diamond expert-level physics, chemistry, biology; higher accuracy = better : | Rank | Model | Score | Params | |---|---|---|---| | 1 | Darwin-180B-RSI | 94.4% | 180B | | 2 | Kimi-K3 | 93.5% | 2.8T | | 3 | Darwin-397B-ZTC | 93.4% | 404B | | 4 | Ornith-1.5-397B | 92.8% | 403B | | 5 | Qwen3.8-2.4T-A95B | 92.6% | 2.4T | The GPQA result is particularly notable: Darwin-180B-RSI achieves 94.4% using majority voting over up to 16 samples, while the 3 model, Darwin-397B-ZTC also a VIDRAFT model , achieves 93.4% with greedy decoding and no voting — a different inference regime entirely. The model is listed under the Hugging Face organization FINAL-Bench with the repository FINAL-Bench/Darwin-180B-RSI . If the weights are publicly available there, you can browse or download via the standard Hugging Face CLI: huggingface-cli download FINAL-Bench/Darwin-180B-RSI Access details beyond what is on the model card — including any API endpoints, licensing terms, or inference service availability — are not confirmed in the source material. Check the model card directly for the most current access instructions. Q: How does Darwin-180B-RSI beat models with 10× more parameters on MMLU-Pro? A: The source doesn't explain internal architecture, but the evaluation configuration gives a clue: the model uses a 131,072-token thinking budget, suggesting extended chain-of-thought reasoning. Parameter efficiency at inference is a known benefit of well-optimized mixture-of-experts or distillation-based architectures, though VIDRAFT has not publicly confirmed which approach is used here. Q: Are the GPQA and MMLU-Pro scores independently verified? A: AIhoster explicitly states it does not recompute scores — it aggregates from public model cards and leaderboard sources, matched by exact repository ID. The source for Darwin-180B-RSI's scores is listed as its own model card. Independent third-party reproduction has not been confirmed in this reporting. Q: Can I use Darwin-180B-RSI commercially? A: Licensing terms are not specified in the source article. Check the model card at FINAL-Bench/Darwin-180B-RSI on Hugging Face for the applicable license before any production or commercial use. Originally reported by AIhoster 러시아 2026-10-18 — source article https://aihoster.ru/benchmarks .